TY - GEN
T1 - Latent Topic-Aware Multi-label Classification
AU - Ma, Jianghong
AU - Liu, Yang
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - In real-world applications, data are often associated with different labels. Although most extant multi-label learning algorithms consider the label correlations, they rarely consider the topic information hidden in the labels, where each topic is a group of related labels and different topics have different groups of labels. In our study, we assume that there exists a common feature representation for labels in each topic. Then, feature-label correlation can be exploited in the latent topic space. This paper shows that the sample and feature exaction, which are two important procedures for removing noisy and redundant information encoded in training samples in both sample and feature perspectives, can be effectively and efficiently performed in the latent topic space by considering topic-based feature-label correlation. Empirical studies on several benchmarks demonstrate the effectiveness and efficiency of the proposed topic-aware framework.
AB - In real-world applications, data are often associated with different labels. Although most extant multi-label learning algorithms consider the label correlations, they rarely consider the topic information hidden in the labels, where each topic is a group of related labels and different topics have different groups of labels. In our study, we assume that there exists a common feature representation for labels in each topic. Then, feature-label correlation can be exploited in the latent topic space. This paper shows that the sample and feature exaction, which are two important procedures for removing noisy and redundant information encoded in training samples in both sample and feature perspectives, can be effectively and efficiently performed in the latent topic space by considering topic-based feature-label correlation. Empirical studies on several benchmarks demonstrate the effectiveness and efficiency of the proposed topic-aware framework.
KW - Feature-label correlation
KW - Multi-label learning
KW - Sample and feature extraction
KW - Topic
UR - https://www.scopus.com/pages/publications/85097100703
U2 - 10.1007/978-3-030-58568-6_33
DO - 10.1007/978-3-030-58568-6_33
M3 - 会议稿件
AN - SCOPUS:85097100703
SN - 9783030585679
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 558
EP - 573
BT - Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings
A2 - Vedaldi, Andrea
A2 - Bischof, Horst
A2 - Brox, Thomas
A2 - Frahm, Jan-Michael
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th European Conference on Computer Vision, ECCV 2020
Y2 - 23 August 2020 through 28 August 2020
ER -